mirror of
https://github.com/k2-fsa/icefall.git
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138 lines
3.9 KiB
Python
Executable File
138 lines
3.9 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (author: Liyong Guo)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import logging
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import os
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from pathlib import Path
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from lhotse import load_manifest
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from lhotse.dataset import (
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BucketingSampler,
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K2SpeechRecognitionDataset,
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)
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from torch.utils.data import DataLoader
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from icefall.utils import setup_logger
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import torch
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import quantization
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def get_parser():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--bytes-per-frame",
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type=int,
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default=4,
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help="The number of bytes to use to quantize each memory embeddings"
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"Usually, it's equal to number codebooks",
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)
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parser.add_argument(
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"--memory-embedding-dim",
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type=int,
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default=1024,
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help="dim of memory embeddings to train quantizer",
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)
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parser.add_argument(
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"--mem-dir",
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type=Path,
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default="conformer_ctc/exp/mem",
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help="The experiment dir",
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)
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parser.add_argument(
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"--mem-layer",
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type=int,
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default=None,
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help="which layer to extract memory embedding"
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"Specify this manully every time incase of mistakes",
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)
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return parser
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def initialize_memory_dataloader(mem_dir: Path = None, mem_layer: int = None):
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assert mem_dir is not None
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assert mem_layer is not None
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mem_manifest_file = mem_dir / f"{mem_layer}layer-memory_manifest.json"
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assert os.path.isfile(
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mem_manifest_file
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), f"{mem_manifest_file} does not exist."
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cuts = load_manifest(mem_manifest_file)
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dataset = K2SpeechRecognitionDataset(return_cuts=True)
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max_duration = 1
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sampler = BucketingSampler(
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cuts,
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max_duration=max_duration,
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shuffle=False,
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)
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dl = DataLoader(dataset, batch_size=None, sampler=sampler, num_workers=4)
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return dl
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def main():
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parser = get_parser()
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args = parser.parse_args()
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assert args.mem_layer is not None
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setup_logger(f"{args.mem_dir}/log/quantizer_train")
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trainer = quantization.QuantizerTrainer(
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dim=args.memory_embedding_dim,
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bytes_per_frame=args.bytes_per_frame,
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device=torch.device("cuda"),
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)
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dl = initialize_memory_dataloader(args.mem_dir, args.mem_layer)
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num_cuts = 0
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done_flag = False
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epoch = 0
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while not trainer.done():
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for batch in dl:
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cuts = batch["supervisions"]["cut"]
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embeddings = torch.cat(
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[
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torch.from_numpy(c.load_custom("encoder_memory"))
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for c in cuts
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]
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)
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embeddings = embeddings.to("cuda")
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num_cuts += len(cuts)
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trainer.step(embeddings)
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if trainer.done():
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done_flag = True
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break
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if done_flag:
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break
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else:
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epoch += 1
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dl = initialize_memory_dataloader(args.mem_dir, args.mem_layer)
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quantizer = trainer.get_quantizer()
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quantizer_fn = (
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f"{args.mem_layer}layer-"
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+ quantizer.get_id()
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+ f"-bytes_per_frame_{args.bytes_per_frame}-quantizer.pt"
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)
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quantizer_fn = args.mem_dir / quantizer_fn
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torch.save(quantizer.state_dict(), quantizer_fn)
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if __name__ == "__main__":
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logging.getLogger().setLevel(logging.INFO)
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main()
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